Control system database systems and methods

ABSTRACT

The embodiments described herein include one embodiment that provides a control method that includes connecting a first controller to a control system; receiving control system configuration data from a database, in which the configuration data comprises holistic state data of a second controller in the control system; and configuring operation of the first controller based at least in part on the configuration data received.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/077,006, which was filed on Nov. 11, 2013, which is incorporated byreference herein in its entirety for all purposes.

BACKGROUND

The present disclosure generally relates to control systems, and moreparticularly, to the use of a database within a control system decisioncycle.

Generally, a control system, in an industrial plant, complex process, orsystem, may involve a complex decision making process to determine theproper control action for a given control cycle. For example, in amodel-based control system (e.g., a model predictive control system),the decision making process may involve a systematic search process(e.g. optimization search) based on process models, operationalconstraints, and decision bounds, which may all be functions ofoperational conditions of the process being controlled. The need forcomputational efficiency has generally resulted in embedding thesefunctions explicitly within the optimization problem formulation. As aconsequence any change to these functional dependencies may requirereformulation of the optimization problem.

Another instance of a complex decision making process arises when thecontrol system includes multiple interrelated controllers. For example,a first controller (e.g., a model predictive controller) may perform anoptimization search to determine a control action to be taken during acontrol cycle. In performing the optimization search, the firstcontroller may utilize an operational parameter of the control system,such as temperature of a boiler, as an input variable. In some systems,the operational parameter may be determined by a second controller inthe control system. Thus, the first controller may request the desiredoperational parameter from the second controller. However, as can beappreciated, the second controller may also perform functions, such asits own optimization search or gathering operational parameters. Inother words, the first controller may interrupt the operation of thesecond controller to obtain the desired operational parameter, which mayreduce the efficiency of the second controller and the control systemoverall.

More generally, many systems utilize various automation components, suchas controllers, that operate virtually independently, but the actions ofwhich may affect other elements of a controlled machine, plant orprocess. In some environments, logs or shared databases are used tostore various values during control, as well as error notices, and soforth. Such storage approaches, however, are limited to accessing thedata at the beginning of a decision cycle by the individual controllers.For example, in a model predictive controller, the data is read only atthe beginning of the optimization process. The existing paradigm fordata access is historically dictated by often relatively slow andcumbersome access to the data, particularly for data that is needed bythe same controller over time, and data useful for other controllers.

Accordingly, it would be beneficial to devise a new architecture for thecontrol system that enhances data communication efficiency within thecontrol system. For example, the data storage, access, and utilizationmay be altered to yield new paradigms for large scale distributedcontrol systems that are potentially deployed with a large geographicalfootprint.

BRIEF DESCRIPTION

Certain embodiments commensurate in scope with the originally claimedinvention are summarized below. These embodiments are not intended tolimit the scope of the claimed invention, but rather these embodimentsare intended only to provide a brief summary of possible forms of theinvention. Indeed, the invention may encompass a variety of forms thatmay be similar to or different from the embodiments set forth below.

A first embodiment provides a control method that includes connecting afirst controller to a control system; receiving control systemconfiguration data from a database, in which the configuration datacomprises holistic state data of a second controller in the controlsystem; and configuring operation of the first controller based at leastin part on the configuration data received.

A second embodiment provides a control method that includes determiningan intermediate search result in a first controller during anoptimization search; receiving data from a database, wherein the datacomprises data determined by and written to the database by the samecontroller at a previous time or by a second controller; and determininga next search result in the optimization search based at least in parton the data received.

A third embodiment provides a control system that includes a firstcontroller; a second controller; and a database communicatively coupledto the first controller and second controller, wherein the database isconfigured to facilitate communication of data from the first controllerto the second controller by storing the data received from the firstcontroller; and in response to a request from the second controller,transmitting the stored data to the second controller, in which thesecond controller is configured to configure its operation based atleast in part on the stored data.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 depicts a block diagram of a control system utilizing a database,in accordance with the present disclosure;

FIG. 2 depicts a block diagram of a database module coupled to abackplane, in accordance with the present disclosure;

FIG. 3 depicts a block diagram of an in-memory database included in acontroller, in accordance with the present disclosure;

FIG. 4A depicts a flow chart describing a general process for storingdata, in the database, in accordance with the present disclosure;

FIG. 4B depicts a flow chart describing a general process for retrievingdata from the database, in accordance with the present disclosure;

FIG. 5 depicts a block diagram of a database storing holistic state dataof multiple controllers, in accordance with the present disclosure;

FIG. 6 depicts a block diagram of the holistic state data, in accordancewith the present disclosure; and

FIG. 7 depicts a flow chart describing a process for storing holisticstate data in the database, in accordance with the present disclosure;

FIG. 8 depicts a flow chart describing a process for configuring acontroller based on the control system architecture, in accordance withthe present disclosure;

FIG. 9 depicts a flow chart describing a process for performingdiagnostics on the control system, in accordance with the presentdisclosure;

FIG. 10 depicts a flow chart describing a process for an optimizationsearch that utilizes operational parameters stored in the database, inaccordance with the present disclosure;

FIG. 11 depicts a graphical representation of the optimization searchdescribed in FIG. 10, in accordance with the present disclosure;

FIG. 12 depicts a flow chart describing a process for an optimizationsearch that utilizes search branch results stored in the database, inaccordance with the present disclosure; and

FIG. 13 depicts a graphical representation of the optimization searchdescribed in FIG. 12, in accordance with the present disclosure.

DETAILED DESCRIPTION

One or more specific embodiments of the present invention will bedescribed below. In an effort to provide a concise description of theseembodiments, all features of an actual implementation may not bedescribed in the specification. It should be appreciated that in thedevelopment of any such actual implementation, as in any engineering ordesign project, numerous implementation-specific decisions must be madeto achieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

When introducing elements of various embodiments of the presentinvention, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

As discussed above, control systems may include multiple interrelatedautomation components, such as controllers. More specifically,controllers may utilize operational parameters (e.g., temperature,pressure, or flow rate) either directly measured, or virtually measured,or determined by other controllers. Additionally, controllers may employthe computing power of other controllers to perform functions. Forexample, a first controller (e.g., a hybrid model predictive controller,for which the decision variables include both integer and continuousvariables and the search process solves a mixed integer linear ornonlinear programming problem) may utilize a second controller todetermine a search result for a first optimization search branch and athird controller to determine a search result for a second optimizationsearch branch. The first controller may then compare the search resultsreturned by the second and third controllers to select the better (e.g.,less costly) search result as the criterion for its branching functionin the course of its mixed integer linear or nonlinear optimization.

Thus, the operation of the control system may be based on the ability tocommunicate data between the various controllers in the system. Forexample, based on the examples described above, the data communicatedmay include operational parameters and/or search results. Additionally,as can be appreciated, to gather such data, a controller may need todetermine the configuration (e.g., architecture) of the rest or at leastpart of the rest of the control system. For example, the firstcontroller may determine that the second controller determines thedesired operational parameter. Similarly, the first controller maydetermine that the second controller has the ability (e.g., appropriatecomputing power, model, and/or objective function) to perform theoptimization search down the first search branch and that the thirdcontroller has the ability to perform the optimization search down thesecond search branch.

In certain embodiments, an optimal control trajectory required tocontrol a process or a system may be determined by solving thefollowing:min(u _(k) ,z _(k))Γ(ŷ _(k) ,ŷ _(k) ^(Trajectory)),subject to:w _(k) =f(u _(k) ,z _(k),ρ);θ_(k) =f(u _(k) ,z _(k) ,w _(k));x _(k) =F _(k)(u _(k) ,z _(k) ,x _(k−1),θ_(k));y _(k) =G _(k)(u _(k) ,z _(k) ,x _(k),θ_(k)); andL<u _(k) <H;where Γ( ) is the cost function capturing the control objectives, ŷ_(k)is the controlled variable outputs (ŷ∈y), and ŷ_(k) ^(Trajectory) is anexplicit or implicit representation of a desired output variabletrajectory, u_(k) is the continuous decision variable (e.g., amanipulated variable), and z_(k) is the discrete decision variable (e.g.an on/off decision on whether a given unit should be on or off). Inaddition, constraints (e.g., L and H) may be trajectory functions. Theminimization of the above objective function is achieved throughadjustments to the continuous decision variables u_(k), and discretedecision variables z_(k). Note that the optimization problem above ismerely illustrative and is not intended to be limiting. For example, inother embodiments, the objective function Γ( ) may be defined to includepenalties on decision variables u_(k) and z_(k). In the absence of z_(k)the optimization problem may be reduced to the commonly formulated modelpredictive control solution without integer decision variables.

Based on the above, the system under control is modeled as a parametrichybrid model defined by a general state space description where theevolution of state x_(k) is defined as x_(k)=F_(k)(u_(k), z_(k),x_(k−1), θ_(k)) and the output of the system is described asy_(k)=G_(k)(u_(k), z_(k), x_(k), θ_(k)) where F_(k) and G_(k) are ingeneral nonlinear functions of state x_(k), decision variables u_(k) andz_(k), and parameter vector θ_(k) that captures system parameters thatare not decision variables for the optimizations problem or outputs ofthe process, but whose value may impact behavior of the process (e.g., areaction rate that impacts the outcome of a chemical reaction but isneither an input to the reaction nor a product of the reaction). In theparametric hybrid model above, the parameter θ_(k) is explicitly modeledas θ_(k)=f(u_(k), z_(k), w_(k)) and hence the value of θ_(k) may changeautomatically as a function of operating condition of the system.Furthermore, θ_(k) is potentially a function of w_(k) which is theoutput of another model that may be purely empirical (e.g., black box).

To minimize the optimization objective Γ(ŷ_(k), ŷ_(k) ^(Trajectory)), ingeneral, a mixed integer nonlinear (or linear) programming algorithm isneeded. An important step in this search process is often a branch andbound procedure where the optimizer discards large subsets of candidatesolutions based on estimates on the quality of the candidate solutions.In some embodiments, the search algorithm may calculate the estimatesfor the quality of the candidate solutions internally. In a large scalecontrol system however, it is possible to use a second controller (or anestimate by that same controller at an earlier time) to estimate thequality of the presently viable candidate solutions. In one embodimentof this disclosure, we propose the use of an entry in a database (e.g.,made by the controller at a previous time or by a second controller) asa mechanism for communicating the necessary information for branchingoperation in the course of a search process (e.g., optimization search)in real time.

In some embodiments, the controllers may retrieve desired data directlyfrom one another. For example, the first controller may request the datafrom the second controller. In response to the request, the secondcontroller may gather the requested data (e.g., stored in memory) andsend the requested data back to the first controller. As can beappreciated, the second controller may perform various functions, suchas controlling connected devices. Thus, to gather and send the requesteddata may interrupt the current operation of the second controller, whichmay reduce the efficiency of the second controller and the controlsystem as a whole.

Accordingly, embodiments of the present disclosure utilize a database toimprove the efficiency of data transfer within the control system. Forexample, one embodiment describes a control method that includesdetermining an intermediate search result in an optimization search in afirst controller; receiving data from a database, in which the datacomprises data determined by and written to the database by a secondcontroller; and determining a next search result in the optimizationsearch based at least in part on the data received. Another embodimentdescribes a control method that includes connecting a first controllerto a control system; receiving control system configuration data from adatabase, in which the configuration data comprises holistic state dataof a second controller in the control system; and configuring operationof the first controller based at least in part on the configuration datareceived. In some embodiments, the holistic state data of the secondcontroller may include current and past measurements of the processinputs/outputs variables, status information (e.g., including status formeasurements, unit operation status, solver status at the end of searchprocess), control matrix structure, model, & parameters, optimizationproblem formulation (e.g., including objective function, constraintsset), and predictions of the controller action, or any combinationthereof. Holistic state data may be communicated between differentmodules using an intelligent communication protocol that may improvedata transfer efficiency (e.g., transferring only changes to theholistic state data).

In other words, a controller may gather desired data from the database,for example during an optimization search (e.g., decision making loop),instead of directly from the other controllers in the control system.That is, the database may standardize communication between thecontrollers in the control system. Accordingly, the communicationefficiency of the control system may be improved because theinterruption of controller operations to request data may be reduced.Additionally, control system security may be more easily managed becausesecurity improvement efforts may be more heavily focused on thedatabase. For example, management of the database may be restricted toauthorized personnel. Furthermore, the modularity and integration (e.g.,between different controller types) within the control system may beimproved because interactions between the various controllers in thecontrol system are standardized through the database. For example,components in the control system may be changed/replaced (e.g., duringmaintenance or diagnostics) without interrupting controller execution.Moreover, timing issues within the control system may be reduced becausedata may asynchronously be communicated between controllers via thedatabase. That is, controllers may avoid synchronizing their respectiveclocks (e.g., timing) to communicate data.

The state (e.g., holistic state data) of each controller in the controlsystem may also be captured. More specifically, instead of merelycapturing the previous input/output variables, the state of thecontroller may include the controller's model, objective function, inputvariables, output variables, constraints, a control matrix, a status,predicted controller actions, controller settings, an optimizationstate, or any combination thereof. The additional state data capturedmay improve diagnostic analysis of the control system. For example, thecontrol system's response to an event (e.g., addition of a catalyst to aboiler) may be analyzed to determine whether the controller model'saccurately accounted for the event. Additionally, a controller mayself-configure (e.g., determine which controller to utilize forbranching in a search process) based on the state data of the othercontrollers in the control system. In other words, the controller maydetermine the architecture of the control system (e.g. connectivity of adistributed control system) and configure itself accordingly. Thus, insome embodiments, the state data for the controllers in the controlsystem may be stored in a database and provided to individualcontrollers to enable them to self-configure. Moreover, in someembodiments, the state of the controllers 14 may be captured and storedin a standardized format, which may improve the modularity andintegration within the control system 10 because each component mayutilize (e.g., understand) the same state formatting.

By way of introduction, an embodiment of a control system 10 (e.g.,model-based control system or distributed control system) is depicted inFIG. 1. As depicted, the control system 10 may include multiple users 12(e.g., User 1 . . . User N) communicatively coupled to variouscontrollers 14 (e.g., Controller 1 . . . Controller M), and a server 16communicatively coupled to the controllers 14. The controllers 14described herein may include model predictive controllers, modelesscontrollers, proportional-integral-derivative controllers, and the like.In some embodiments, the components (e.g., users 12, controllers 14, andserver 16) in the control system 10 may be communicatively coupled toform a network such as an EtherNet/IP network, a ControlNet network, aDeviceNet network, a Data Highway Plus network, a Remote I/O network, aFoundation Fieldbus network, a Serial, DH-485 network, a SynchLinknetwork, or any combination thereof.

More specifically, each of the users 12 may interact with the variouscontrollers 16 via a human-machine-interface, such as a desktopcomputer, a laptop computer, or tablet computer. For example, a user 12may instruct a controller 14 to perform a specific control action (e.g.,turn on a boiler) or to view operational parameters of the controlsystem 10 (e.g., temperature of the boiler). To facilitate suchoperations, the controllers 14 may include one or more processor 18 andmemory 20. The memory 20 may store data gathered/calculated by thecontroller 14 and/or non-transitory machine readable instructions thatmay be performed by the processor 18.

Additionally, the control system 10 may include a database 22. Asdepicted, the database 22 may be optionally located within a controller14, the server 22, as a standalone module, or any combination thereof.In other words, the database 22 may be a network database 22A, adatabase module 22B, or an in-memory database 22C. The database 22 mayutilize a storage device, such as a flash drive, random access memory(RAM), a disk drive, a tape drive, or the like. However, as describedabove, the database 22 may be included (e.g., accessed) in a decisionmaking loop (e.g., optimization search). Thus, the type of storagedevice selected for the database 22 may be dependent on the controlcycle (e.g., update speed) of the process being controlled. For example,if the control cycle is only 5 milliseconds, a faster storage device,such as RAM, may be utilized. On the other hand, if the control cycle is5 minutes, a slower storage device, such as a disk drive, may beutilized. In some embodiments, a combination of storage drives, forexample RAM in combination with a disk drive, may be utilized toaccommodate a fast process (e.g., short control horizon). For example,the database 22 may include an in-memory database 22C (e.g., RAM) incombination with a network database 22A (e.g., a disk drive). In such anembodiment, the in-memory database 22C may act as a first-in-first-outbuffer. In other words, the in-memory database 22C may store a fixedamount (e.g., 5 minutes worth) of data, and as new data is stored, theoldest data may be transmitted to and stored in the network database22A.

As described above, the database 22 may facilitate communication betweencontrollers 14 within the control system 10. Illustratively, when thecontrol system 10 utilizes a network database 22A, each controller 14may transmit data through the network to the server 16, such that thedata is written into the network database 22A. Similarly, the server 16may retrieve data from the network database 22A and transmit it to thecontrollers 14 via the network. Accordingly, each database 22 mayinclude one or more dedicated processing components 23 to facilitateperforming such functions (e.g., writing to database 22, searchingdatabase 22, and transmitting data from database 22).

Additionally or alternatively, the control system 10 may utilize adatabase module 22B, as depicted in FIG. 2. As in the depictedembodiment, the database module 22B may be included as a module onchassis, such as a ControlLogix chassis, made available by RockwellAutomation of Milwaukee, Wis. Accordingly, the database module 22B maybe communicatively coupled to other modules on the chassis 24, includingcontroller modules 14A (e.g., Controller Module 1 . . . ControllerModule M−1) and an input/output module 26, via a communication/powerbackplane 28. In some embodiments, the communication/power backplane 28may supply electrical power to each of the connected modules.Additionally, the communication/power backplane 28 may provide acommunication interface (e.g., network or bus) between each of theconnected modules. Thus, each of the controller modules 14A may transmitdata through the communication/power backplane 28 to the database module22B, such that the data is written into the database module 22B.Similarly, the database module 22B may retrieve data and transmit it tothe controller modules 14 via the communication/power backplane 28.

Furthermore, the input/output module 26 may enable communication betweenmodules coupled directly to the chassis 24 and external devices. Forexample, in the depicted embodiment, an external controller 14B (e.g.,Controller M) may communicate with the database module 22B via theinput/output module 26. More specifically, the external controller 14Bmay interface with the input/output module 26, and the input/outputmodule 26 may interface with the database module 22B via thecommunication/power backplane 28. Thus, the external controller 14B maytransmit data through the input/output module 26 and thecommunication/power backplane 28 to the database module 22B, such thatthe data is written into the database module 22B. Similarly, thedatabase module 22B may retrieve data and transmit it through thecommunication/power backplane 28 and the input/output module 26 to theexternal controller 14B.

As described above, the control system 10 may also utilize an in-memorydatabase 22C, as depicted in FIG. 3. In some embodiments, the in-memorydatabase 22C may utilize portions of the controller's memory 20.Additionally, to reduce the interruption to the operation of thecontroller 14, a core of the controller processor 18 or an entireprocessor 18 may be dedicated to operations of the database 22.

Generally, the controller 14, and more specifically the processor 18,may perform various functions, such as model predictive control 30,runtime calculations 32, and a simulator 34. As depicted, each of thesefunctions may communicate data with the in-memory database 22C, forexample via a bus that communicatively couples the controller processor18 and memory 20. As an illustrative example, the model predictivecontrol 30 may receive an operational parameter from the in-memorydatabase 22C, predict a control action based at least in part on theoperational parameter, and store the predicted control action in thein-memory database 22C. Similarly, the simulator 34 may receiveoperational parameters and/or captured controller states (e.g., holisticstate data) to simulate operation of a controller 14 and/or the controlsystem 10. The results of the simulation may also be stored in thein-memory database 22C to enable further analysis and diagnostics.

In addition to communicating data between the various functions includedin the controller 14, the in-memory database 22C may enablecommunication of data with or between external controllers 14B. Forexample, a first external controller 14B (e.g., Controller M−1) maystore a determined operational parameter in the in-memory database 22C,and the in-memory database 22C may retrieve the operational parameterand transmit it to a second external controller 14B (e.g., ControllerM). In other words, although the in-memory database 22C may be includedwithin the controller memory 14, the operation of the in-memory database22C may be relatively independent from the operation of the controller14.

In each of the database embodiments described above, the databases 22may facilitate communication of data between various components in thecontrol system 10. More specifically, the communication of data mayinclude transmitting data from a first controller to the database 22,and transmitting the data from the database 22 to a second controller.Accordingly, FIG. 4A describes a process 36 for communicating data tothe database 22. As depicted, the process 36 may begin by determiningdata to be stored in the database 22 (process block 38). For example, afirst controller may gather an operational parameter (e.g., temperature)from a sensor monitored by the controller. The data may then betransmitted to the database 22 (process block 40). For example, thefirst controller may transmit the data over the network or bus thatcommunicatively couples the first controller and the database 22. Thedatabase 22 may then store the data in the database 22 (process block42). In some embodiments, the first controller may specify a memoryaddress at which to store the data.

Additionally, process 36 may be periodically (e.g., cyclically) repeated(represented by arrow 43). In other words, data stored in the database22 may be periodically updated, which may include overwriting or addingto previously stored data, which may facilitate diagnostics. Forexample, the first controller may periodically add to the operationalparameter stored in the database 22 to provide a historical view of thechanges in the operational parameter. In some embodiments, the periodicupdating 43 may be based on a timer. In other embodiments, the periodicupdating 43 may be based on a triggering event, such as a change in theoperational parameter monitored by the first controller.

As can be appreciated, each of the controllers 14 in the control system10 may utilize process 36 to write data to the database 22. In someembodiments, the updating frequency of the controllers 14 may vary. Forexample, the first controller may update a flow rate of a pipe every 5millisecond to account for rapid changes in the flow rate, whereas asecond controller may update temperature of a boiler every 5 seconds dueto the slower changes in temperature.

On the other side, FIG. 4B describes a process 44 for receiving datafrom the database 22. As depicted, the process 44 may begin byrequesting data from the database 22 (process block 46). For example,the second controller may request the operational parameter stored inthe database 22 by the first controller. Depending on the functionalityof the database 22, the second controller may request the operationalparameter with varying levels of specificity. For example, the secondcontroller may simply request a particular operational parameter,specify operational parameters gathered by the first controller, orspecify the memory location within the database 22 utilized by the firstcontroller.

The database 22 may then search for the requested data (process block48). If the requested data is found, the database 22 may transmit therequested data (process block 50). For example, the database 22 maytransmit the requested data over the network or bus that communicativelycouples the database 22 and the second controller. In some embodiments,if the requested data is not found, the database 22 may return an errormessage. Thus, by utilizing process 36 and process 44, data from thefirst controller may be communicated to the second controller via thedatabase 22.

As described above, in addition to merely storing operationalparameters, the database 22 may store captured states of controllers 14,as depicted in FIG. 5. More specifically, the database 22 may store theholistic state data for various controllers 14 in the control system 10.For example, in the depicted embodiment, the database 22 stores theholistic state data for Controller 1 (block 52) through Controller M(block 54). Additionally, the database 22 may store the holistic statedata for each controller 14 captured at various time. For example, inthe depicted embodiment, the database 22 stores the holistic state datafor Controller 1 at Time 1 (block 52) through Time T (block 56). As usedherein, “holistic state data” is intended to describeoperation/characteristics of a controller 14 (e.g., controller model)that describe the architecture of the control system, which for examplemay be utilized by other controllers 14 to self-configure, or may beutilized to perform diagnostics, for example by simulating operation ofthe controller 14.

As described above, the controller holistic state data stored in thedatabase 22 may be stored in a standardized format, which may improvethe ease of integration and modularity of the control system 10. Morespecifically, components (e.g., controllers 14) may be easily addedand/or replaced because each component may be configured to utilize thestandardized holistic state data format. One embodiment of standardizedholistic state data 56 is depicted in FIG. 6. As depicted, the holisticstate data 56 includes a control matrix field 58, an input variablefield 60, an output variable field 62, a constraints field 64, a modelfield 66, an objective function field 68, a predicted control actionsfield 70, a controller settings field 72, an optimization state field74, and a status field 76.

More specifically, the holistic state data 56 may capture the operationof the controller 14. For example, the past and current input variablesto the process may be captured by the input variables field 60, and thepast and current output variables from the process may be captured bythe output variables field 62. In some embodiments, the output variablesmay additionally include operational parameters gathered by thecontroller 14, for example from a sensor monitored by the controller 14.In model based controllers, the input variables may include controlledvariables and the output variables may include manipulated variables.For a dynamic system with a modeled delay between an input variable andan output variable, the holistic state data may additionally include themeasurements of the input/output variables for the time delay interval,for example, between [t0−D, t0] where t0 indicates current time and Dindicates the delay length.

Similarly, the constraints field 64 may capture the past and currentconstraints on the input/output variables, and the predicted controlactions field 56 may capture control actions predicted by the controller14 and the predicted response to the control actions. Additionally, theoptimization state field 60 may capture parameters associated with anoptimization search, such as intermediate search results and the statusof the search, and the status field 62 may capture the status of thecontroller 14 and/or the devices (e.g., sensors) controlled or monitoredby the controller. For example, this may include an indication that asensor monitored by the controller is malfunctioning.

The holistic state data 56 may also capture the characteristics of thecontroller 14. For example, the model field 66 may capture a model of aprocess that describes a relationship between the input variables andoutput variables. In some embodiments, the model may include a controlmatrix, which may be captured by the control matrix field 58. Theobjective function field 68 may similarly capture an objective (e.g.,cost) function associated with the process. In some embodiments, theobjective function field 68 may further include parameters used toformulate the objective function, which may facilitate understanding ofselected control actions for performance evaluation or diagnostics. Thecontroller settings field 58 may capture settings used by the controller14, such as the interrelationships (e.g., hierarchy or cascade) withother controllers 14 and processing capabilities.

Since the state data is standardized, holistic state data for eachcontroller may include each of these fields regardless of the controllertype. For example, the holistic state data 42 for a modeless controller(e.g., PID controller) may populate the model field 50 with “0” or“null” to indicate that the controller 14 does not contain a model. Inother embodiments, the standardized holistic state data 42 may includedifferent fields, which may depend on the type of control system 10and/or the type of controllers 14 utilized in the control system 10.

Additionally, since the holistic state data is standardized throughoutthe control system 10, the fields in the holistic state data may utilizevarious short-hand techniques to reduce the burden on the database 22.For example, instead of storing the entire objective function, theobjective function field 68 may store the objective function as a stringor simply store the coefficients of the objective function. Similarly,the control matrix field 58 may store the size of the control matrix andthe values of the matrix as a string. Since the formatting isstandardized, a controller 14 receiving the standardized holistic statedata may reconstruct the desired data. For example, the controller 14may reconstruct the control matrix by parsing the string of values basedon the size of the control matrix (e.g., place first value in string infirst location of matrix and so on).

Furthermore, in some embodiments, standardized holistic state data mayenable standardized visualizations. Illustratively, in a control systemfor a boiler system, controllers may recognize that operationalparameters relate to the boiler system and thus depict a boilervisualization. For example, the boiler visualization may include a pipecoupled to the boiler, and the pipe may be labeled with its flow rate.

In some embodiments, controllers 14 may store their holistic state datain the database 22. One embodiment of a process 78 for storing holisticstate data is described in FIG. 7. As depicted, the process 78 may beginwhen a controller 14 is connected to the control system 10 (processblock 80). For example, the controller 14 may be connected to thecontrol system 10 when the controller 14 is plugged into to apower/communication backplane 28. The controller 14 may then determineits holistic state data (process block 82). More specifically, this mayinclude gathering data (e.g., model, objective function, etc.) andpopulating the holistic state data fields (e.g., model field 66,objective function field 68, etc) with the data. In some embodiments,such data may be stored within the controller memory 20 or a device(e.g., sensor) controlled by the controller 14. The holistic state datamay then be transmitted to and stored in the database 22 (process block84). After the holistic data is stored in the database 22, thecontroller 14 may begin or resume performing control operations (processblock 86). For example, the controller 14 may begin or resume monitoringconnected devices (e.g., sensors), controlling connected devices (e.g.,actuators), performing a different optimization search, or performing acontrol action determined by the optimization search (e.g., optimumsearch result).

In addition to capturing the holistic state data when the controller 14is initially connected, the controller 14 may also periodically (e.g.,cyclically) capture updated holistic state data (represented by arrow88). Similar to the periodic updating described above, updating theholistic state data may include overwriting or adding to previouslystored holistic state data. As will be described in more detail below,adding to the previously stored holistic state data may facilitatediagnostics by enabling the controller at the time the holistic statedata was captured to be recreated (e.g., simulated). Additionally, thecontroller 14 may periodically update 88 its holistic state data basedon a timer (e.g., execution frequency of the control system) or atriggering event, such as a change in input variables. In someembodiments, the controller 14 may update only the portions of theholistic state data that change, which may reduce burden on the database22. Furthermore, the update frequency for each controller in the controlsystem 10 may also vary, for example based on the function of eachcontroller 14 or the interrelationship between controllers 14.

As described above, a controller 14 may utilize the holistic state datastored in the database 22 to self-configure. One embodiment of a process90 for self-configuring a controller 14 is described in FIG. 8. Asdepicted, the process 90 may begin when a controller requests systemconfiguration data from the database 22 (process block 92). In someembodiments, the system configuration data may include holistic statedata for each of the other controllers 14 in the control system 10. Thedatabase 22 may then search for and return the system configuration datato the controller 14 (process block 94). Based on the received systemconfiguration data, the controller 14 may self-configure (process block96). In some embodiments, the controller may utilize programmaticnavigation (e.g., a web crawler) to process the system configurationdata. For example, a controller may look at the operational parameters(e.g., output variables) captured in the holistic state data todetermine what operational parameters are gathered and by whichcontrollers. Additionally, a controller 14 may look at the model,objective function, controller settings (e.g., interrelationships andcomputing power), or any combination thereof to determine whichcontrollers may be utilized for computations (e.g., optimization branchsearches). The controller 14 may then perform control operations basedon its configuration (process block 98). For example, a first controllermay self-configure to utilize a second controller to perform anoptimization search down a search branch, or the first controller mayconfigure to utilize an operational parameter determined by the secondcontroller.

Additionally, as described above, stored holistic state data may beutilized to facilitate control system diagnostics. More specifically,the controller at a specific time (e.g., Time T) may be later recreatedbased on the holistic state data captured at that time, for example inthe controller or another computing device. An embodiment of a process100 that may be utilized is described in FIG. 9. As depicted, theprocess 100 may begin by requesting holistic state data from thedatabase (process block 102). More specifically, this may includespecifying the controller 14 and time period for the holistic statedata, or a specific memory location storing the holistic state data. Thedatabase 22 may then be searched for and return the requested holisticstate data (process block 104). Based on the holistic state data, thecontroller 14 may be recreated (e.g., configured) enabling operation ofthe controller 14 to be simulated (process block 106) and diagnostics tobe performed on the simulated operation (process block 108). In someembodiments, this may include performing “What If” analysis bysimulating various control actions and analyzing the response.Additionally, in some embodiments, accuracy of the controller may beanalyzed. For example, the accuracy of the controller may be determinedby comparing the predicted response with the actual response to aparticular control action. In some embodiments, this may includedetermining whether the model accurately accounts for events in thecontrol system, such as the addition of a catalyst to a boiler.

In some embodiments, the process 100 may be performed while thecontroller 14 is still in operation. Thus, incorrect or unacceptablebehavior detected during diagnostics may be correct while the controlleris in operation. For example, if it is determined that the controllermodel is inaccurately modeling the process, the controller 14 mayreplace the inaccurate model with a new model.

Based on the techniques described above, FIGS. 10-13 describeillustrative embodiments of a controller 14 utilizing a database 22 asan integral part of a decision loop. More specifically, FIG. 10describes a process 110 for including the database 22 within anoptimization search. As depicted, the process 110 includes initializingan optimization search (process block 112), finding an intermediatesearch result (process block 114), requesting and receiving anoperational parameter from the database 22 (process block 116), andupdating the optimization search based on the received operationalparameter (process block 118). The controller 14 may then find the nextsearch result based on the updated optimization search (arrow 120). Theoptimization search (e.g., process 110) may continue until the search iscompleted (process block 122). However, if the optimization search exitsbefore completion (process block 124), the state of the optimizationsearch may be stored (process block 126). In either case, after exitingthe optimization search, the controller 14 may continue its controloperations (process block 128).

To help illustrate, the process 110 will be described in relation to thegraphical representation of an optimization search depicted in FIG. 11.Accordingly, the controller 14 may initialize the optimization search(process block 112) by determining feasible search results 130,constraints 132 to the feasible search results, and an initial searchresult 134. From the initial search result 134, the controller 14 maythen find an intermediate search result 136 (process block 114). Oncethe intermediate search result 136 is found, the controller 14 may pausethe optimization search and request/receive an operational parameterstored in the database 22 (process block 116), for example throughprocess 44. The controller 14 may then utilize the operational parameterto update the optimization search (process block 118). For example, thecontroller 14 may update the optimization search to account for changesto an input variable (e.g., boiler temperature). The controller 14 maythen resume the optimization search and find the next search result 138based on the updated optimization search (process block 114). Theprocess 110 may continue until an optimum search result 140 is found(process block 122).

On the other hand, if the optimization search exits while at anintermediate search results 136 or 138 (process block 124), the state ofthe optimization search may be stored (process block 126). In someembodiments, this may include storing the intermediate search results,optimization settings, the updated optimization search, and the like inthe optimization state field 74 of holistic state data. Storing thestate of the optimization may enable a “hot start” when the optimizationsearch is resumed. In other words, the optimization search may pick upwhere it left off instead of restarting the entire search.

Additionally, FIG. 11 describes another process 142 for utilizing adatabase 22 in an optimization search, for example hybrid modelpredictive control that includes both continuous and integer decisionvariables. As depicted, the process 142 includes determining the controlsystem configuration (process block 144), initializing an optimizationsearch (process block 146), reaching a branching search result (processblock 148), writing a setpoint for each branch to the database 22(process block 150), requesting and receiving branch search results(process block 152), and selecting a search branch based on the results(process block 154). Along the selected branch, the controller 14 maythen find the next branching search result (arrow 156). Similar toprocess 110, process 142 may exit the optimization search (process block158) before or when an optimum search result is found. In either case,after exiting the optimization search, the controller 14 may continueits control operations (process block 160).

To help illustrate, the process 142 will be described in relation to thegraphical representation of an optimization search depicted in FIG. 13.In some embodiments, the controller 14 may determine the control systemconfiguration (process block 144) by utilizing process 90. Accordingly,the controller 14 may determine which controllers may be utilized in theoptimization search. For example, a first controller may assign a firstsearch branch 162 to a second controller, a second search branch 164 toa third controller, a third search branch 166 to a fourth controller. Inother words, the controller 14 may form a hierarchal relationship (e.g.,cascading interrelationship) with other controllers by assigning searchbranches to subordinate controllers (e.g., second, third, and fourthcontrollers).

The controller 14 may then initialize the optimization search (processblock 146) by determining feasible search results 130, constraints 132to the feasible search results, and an initial search result 134. Fromthe initial search result 134, the controller 14 may then find abranching search result 168 (process block 148). As depicted, threepotential search branches (e.g., 162, 164, and 166) extend out from thebranching search result 168. At the branching search result 168, thecontroller 14 may pause the optimization search and write a setpoint foreach search branch to the database 22 (process block 150). Thesubordinate controllers assigned to each search branch may then retrieveits setpoint, for example through process 44, and perform anoptimization search based on the setpoint. For example, the secondcontroller may determine a first search 168, the third controller maydetermine a second search result 170, and the fourth controller maydetermine a third search result 172. Each subordinate controller maythen write its determined search result to the database 22, for examplethrough process 36. Thus, in some embodiments, the optimization searchdown each search branch may be parallelized to improve search efficiencybecause each subordinate controller may search simultaneously.

The controller 14 may then request/receive the search results for eachsearch branch from the database 22 (process block 152). Based on thesearch results, the controller 14 may resume its optimization search andselect a search branch (process block 154). In some embodiments, thecontroller 14 may utilize the objective function to compare the cost ofperforming each returned search result and choose the least costly. Theprocess 142 may then continue by finding a next branching search result(process block 148) or exit the optimization search (process block 158).

Although the techniques described herein are generally described inrelation to a physical control system as depicted in FIG. 1, thetechniques may additionally be applied to “table top” control systems(e.g., control systems simulated on a computing device). Additionally,the techniques described herein may be utilized locally or remotely(e.g., via the cloud). Furthermore, the techniques described herein mayenable the control system to integrate with an economic optimizationengine, which may determine the overall economic profitability of theoperation. Moreover, although the techniques described herein aregenerally described in relation to controllers 14, the techniques mayadditionally be applied to other components in the control system 10(e.g., server 16, user 12, or other automation device) that may utilizethe data stored in the database 22.

The technical effects of the present disclosure include improvingcommunication efficiency within a control system 10. More specifically,a database 22 may be utilized to standardize communication betweenvarious controllers 14 in the control system 10. Thus, interruption ofcontroller operations to request data may be reduced, securityimprovement efforts may be more heavily focused on the database 22,modularity and integration within the control system 10 may be improved,and timing issues within the control system 10 may be reduced.Furthermore, in some embodiments, the database 22 may store holisticstate data for each controller 14 in the control system 10, which insome embodiments may be standardized for the control system 10. Thus,control system diagnostics may be improved by simulating controlleroperation, controllers in the control system may self-configure, and themodularity and integration within the control system 10 may be improved.

As discussed above, the present techniques are particularly well suitedto applications that utilize model based controllers, although they arenot limited to such applications. In summary, model-based controlsystems have been pervasive in many facets of modern life. Examplesinclude the process industry, the power industry, automotive, oil andgas exploration, aerospace, and even closed loop decision supportsystems used for planning and scheduling. As used herein, model-basedcontrol systems refer to a control system whose output relies at leaston the following two inputs:

-   (i) The current and/or past process measurements (actual and/or    virtual), and-   (ii) The output of a model that may receive process measurements and    produces at least one value that modifies controller structure.    Based on the definition above, a model-less controller (e.g., a PID    controller) may be a model-based control system for which the second    input is zero.

Given the role of the control systems, secure, robust, and timelyoperation of controllers in the control system is important inreal-world applications. More specifically, the performance of a controlsystem based on the following abilities:

-   (i) Ability to assess the quality of the model: The model that is    being used by a controller may play a critical role in the success    of the control strategy. Depending on the application, the models    may be assed online and/or offline-   (ii) Ability to monitor controller performance: The performance of    controllers may be monitored in two different metrics:    -   1) An instantaneous measure of performance (e.g. a running        standard deviation from a desired setpoint), and    -   2) A predictive performance metric where, in addition to the        controlled system behavior, the predicted process response to        planned control actions is used to analyze performance. One        example is performance monitoring for a batch reaction where        both measured and anticipated process responses are used to        quantify controller performance against “a golden batch”        standard.-   (iii) Ability to account for “events”: Events may impact process    behavior but are often not treated as a measured process value. One    example is when an operator introduces a new catalyst to initiate a    transition, which in some embodiments may simply recorded in a    spreadsheet. Another example is when a plant operates under abnormal    conditions. Illustratively, a failed actuator may result in poor    performance by the control system while the control strategy is    optimal for normal operation conditions. Thus, such events may be    accounted for to better monitor performance of the control system.-   (iv) Ability to perform diagnostics: Diagnostics can take different    flavors:    -   1) Detecting that the controller performance is no longer        acceptable.    -   2) Identifying the source of the detected unacceptable        performance.    -   3) Modifying the controller to improve its performance        preferably without completely shutting down the control system.    -   To perform diagnostics, the controllers response may be revisted        for a desired time period in the past. In some embodiments,        inputs may be varied to examine controller behavior under these        altered conditions. It is a significant leap from the state of        the art if this extensive analysis is performed while the        controller is still running with the possibility of changing        controller structure to correct unacceptable behavior.-   (v) Ability to enable asynchronous and simultaneous multi-user    controller access: The ability to do all the activities listed above    by multiple users asynchronously accessing the controller remotely.

Performing the tasks listed above may impose significant demands on datamanagement and data flow efficiency within a control system, and theinnovations presented in the present disclosure may greatly facilitateoperation of the automation systems. As can be appreciated,deterministic execution requirements of the controllers has led tocontrol systems that avoid databases as a node within a controller'sdecision loop (e.g., optimization search). In other words, existingcontrol system architectures are designed with the assumption that acontroller does not query a database to determine its next move (e.g.,control action). The foregoing techniques allow for the use of anintegrated or on-board database that can facilitate automationoperations while avoiding the drawbacks associated with prior paradigmsfor the storage, access and use of automation data.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

The invention claimed is:
 1. An automation system comprising: anactuator configured to implement a control action to facilitateperforming an automation process in the automation system; a sensorconfigured to measure an operational parameter of the actuator; and acontrol system communicatively coupled to the actuator and the sensor,wherein: the control system comprises a plurality of processors andmemory implemented in a plurality of controllers; and the memory isconfigured to store instructions that, when executed by one or moreprocessors of the plurality of processors, cause the one or moreprocessors to: determine holistic state data comprising a first fieldthat indicates input variables to the automation process, a second fieldthat indicates output variables from the automation process, a thirdfield that indicates a process model that describes relationship betweenthe input variables and the output variables of the automation process,a fourth field that indicates constraints on the input variables of theautomation process, and a fifth field that indicates an objectivefunction, wherein a first controller of the plurality of controllers isconfigured to self-configure an interconnection with a second controllerselected from the plurality of controllers based on the holistic statedata; determine the control action to be implemented by the actuatorbased at least in part on the operational parameter of the actuatormeasured by the sensor and the process model, the input variables, theoutput variables, the constraints, and the objective function indicatedin fields of the holistic state data; and instructing the actuator toimplement the control action to facilitate optimizing performance of theautomation process.
 2. The automation system of claim 1, wherein thecontrol system comprises: a chassis comprising a backplane; and a firstcontroller module, wherein: the first controller module comprises thefirst controller; and the first controller module is configured toreceive electrical power and communicate via the backplane when thefirst controller module is connected to the chassis.
 3. The automationsystem of claim 2, wherein the control system comprises a secondcontroller module, wherein the second controller module comprises thesecond controller; and the second controller module is configured toreceive electrical power and communicate with the first controllermodule via the backplane when the first controller module and the secondcontroller module are connected to the chassis.
 4. The automation systemof claim 2, wherein: the control system comprises an input/output moduleconfigured to communicate via the backplane when connected to thechassis; and the second controller is external from the chassis andconfigured to communicate with the first controller module via theinput/output module when the input/output module is connected to thechassis and the second controller is communicatively coupled to theinput/output module via one or more data buses.
 5. The automation systemof claim 1, wherein the holistic state data comprises: first holisticstate data associated with the first controller, wherein the controlsystem is configured to receive the first holistic state data from thefirst controller when the first controller is initially deployed in thecontrol system; and second holistic state data associated with thesecond controller, wherein the control system is configured to receivethe second holistic state data from the second controller when thesecond controller is initially deployed in the control system.
 6. Theautomation system of claim 1, wherein the control system is configuredto provide the holistic state data to the first controller when thefirst controller is initially deployed in the control system to enablethe first controller to self-configure the interconnection with thesecond controller such that the first controller uses the secondcontroller as a subordinate controller.
 7. The automation system ofclaim 1, wherein the holistic state data comprises: a sixth field thatindicates computational capabilities of the plurality of controllersimplemented in the control system; and a seventh field that indicatesinterconnections between controllers of the plurality of controllers,interconnections between the plurality of controllers and one or moreautomation components, or both in the control system.
 8. The automationsystem of claim 1, wherein the control system is configured to:formulate an optimization problem based at least in part on the processmodel, the input variables, the output variables, the constraints, andthe objective function indicated in the fields of the holistic statedata; and determine the control action to be implemented by the actuatorby performing an optimization search to solve the optimization problemformulated by the control system.
 9. The automation system of claim 1,wherein: the first controller comprise a first one or more processorsand first memory; and the second controller comprises a second one ormore processors and second memory.
 10. A tangible, non-transitorycomputer-readable medium that stores instructions executable by one ormore processors of a control system, wherein the instructions compriseinstructions to: instruct, using the one or more processors, the controlsystem to provide control system configuration data to a firstcontroller when the first controller is initially connected in thecontrol system, wherein the control system configuration data comprisesa first field that indicates a process model that describes relationshipbetween input variables and output variables of an automation process, asecond field that indicates constraints on the input variables of theautomation process, a third field that indicates an objective function,and a fourth field that indicates that a second controller implementedin the control system determines an operational parameter of an actuatormeasured by a sensor; self-configure, using the one or more processors,an interconnection between the first controller and the secondcontroller at least in part by selecting the second controller from aplurality of controllers in the control system based at least in part onthe third field of the control system configuration data that indicatesthat the second controller determines the operational parameter of theactuator measured by the sensor; formulate, using the one or moreprocessors, an optimization problem based at least in part on the firstfield of the control system configuration data that indicates theprocess model, the second field of the control system configuration datathat indicates the constraints on the input variables, and the thirdfield of the control system configuration data that indicates theobjective function; determine, using the one or more processors, acontrol action to be implemented by the actuator to facilitateperforming the automation process by solving the optimization problembased at least in part on the operational parameter of the actuatormeasured by the sensor; and instruct, using the one or more processors,the actuator to implement the control action to facilitate optimizingperformance of the automation process.
 11. The tangible, non-transitory,computer-readable medium of claim 10, wherein the instructions toinstruct the control system to provide the control system configurationdata to the first controller comprise instructions to provide thecontrol system configuration data to the first controller when acontroller module comprising the first controller is coupled to abackplane of a control system chassis.
 12. The tangible, non-transitory,computer-readable medium of claim 10, wherein the control systemconfiguration data comprises a sixth field that indicates computingcapability of the second controller.
 13. The tangible, non-transitory,computer-readable medium of claim 10, wherein the control systemconfiguration data comprises a plurality of holistic state data eachcorresponding with one of a plurality of automation components.
 14. Thetangible, non-transitory, computer-readable medium of claim 13, whereinthe instructions to self-configure the interconnection between the firstcontroller and the second controller comprise instructions to parse eachof the plurality of holistic state data.
 15. The tangible,non-transitory, computer-readable medium of claim 10, wherein theinstructions to self-configure the interconnection between the firstcontroller and the second controller comprise instructions toself-configure the interconnection such that the first controller usesthe second controller as a subordinate controller when solving theoptimization problem.
 16. The tangible, non-transitory,computer-readable medium of claim 10, wherein the instructions toinstruct the control system to provide the control system configurationdata to the first controller comprise instructions to retrieve thecontrol system configuration data from a cloud.
 17. A method forperforming diagnostics on a control system comprising: determining,using one or more processors, holistic state data captured by acontroller implemented in the control system, wherein the holistic statedata indicates that the controller determines an operational parameterof an actuator measured by a sensor; simulating, using the one or moreprocessors, operation of the controller based at least in part on theholistic state data while the controller performs one or more otheroperations; analyzing, using the one or more processors, simulation ofthe controller to perform diagnostics on the controller; and adjusting,using the one or more processors, operation of the control system basedat least in part on the diagnostics to enable the control system todetermine a control action that facilitates optimizing performance of anautomation process when implemented by the actuator.
 18. The method ofclaim 17, wherein: simulating the operation of the controller comprisessimulating performance of various control actions by the controller; andanalyzing the simulation of the controller comprises analyzing resultsof the various control actions.
 19. The method of claim 17, whereinanalyzing the simulation comprises: determining a predicted response tothe control action determined by the controller using a process model;and comparing the predicted response with an actual response to thecontrol action to determine accuracy of the process model.
 20. Themethod of claim 19, comprising adjusting the process model when thepredicted response and the actual response vary.